The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study introduces ENTRAP-seq, a high‑throughput in‑planta assay that couples protein‑coding libraries with a nuclear magnetic sorting‑based reporter to multiplexively assess transcriptional regulatory activity of thousands of protein variants. Using this platform and machine‑learning analysis, the authors screened 1,495 plant viral proteins, uncovering numerous novel regulatory domains, and applied machine‑guided, semi‑rational design to modify the activity of a plant transcription factor.

The study used phylogeny‑based analyses of 36 legume genomes and a newly created multiparent advanced generation intercross (MAGIC) population of common bean to predict and characterize genome‑wide deleterious mutations. Machine‑learning integration of conservation and protein features identified thousands of potentially deleterious sites, whose variation correlated negatively with flowering time, maturity, and yield, highlighting the impact of genetic load on breeding performance.

The authors introduce S²-PepAnalyst, a web-based tool that leverages plant-specific datasets and advanced machine learning to predict small signaling peptides (SSPs) with 99.5% accuracy and minimal false negatives. By integrating protein language models, geometric‑topological analysis, and reinforcement learning, the tool surpasses existing predictors such as SignalP 6.0 in classifying peptide families like CLE and RALF.

The study identifies the serine/threonine protein kinase CIPK14/SNRK3.15 as a regulator of sulfate‑deficiency responses in Arabidopsis thaliana seedlings, with mutants showing diminished early adaptive and later salvage responses under sulfur starvation. While snrk3.15 mutants exhibit no obvious phenotype under sufficient sulfur, the work provides a novel proteomic dataset comparing wild‑type and mutant seedlings under sulfur limitation.

The study examined how white lupin (Lupinus albus) cotyledons mobilize nitrogen and minerals during early seedling growth under nitrogen‑deficient conditions, revealing that 60 % of stored proteins degrade within eight days and are redirected to support development. Proteomic analyses showed dynamic shifts in nutrient transport, amino acid metabolism, and stress responses, and premature cotyledon removal markedly impaired growth, highlighting the cotyledon's essential role in nutrient supply and transient photosynthetic activity.

The study characterizes the protein composition of extracellular vesicles (EVs) secreted by the oomycete Phytophthora infestans, revealing enrichment of transmembrane proteins and RxLR effectors, while EV-independent secretions are dominated by cell wall–modifying enzymes. Two MARVEL‑domain proteins, PiMDP1 and PiMDP2, are identified as EV-associated markers that co‑localize with RxLR effectors, with PiMDP2 specifically accumulating at the haustorial interface during early infection, suggesting a role in effector delivery.

The study evaluated 11 intermediate phenotypes linked to stalk lodging resistance in a diverse panel of 566 maize (Zea mays L.) inbred lines across four environments, preserving individual stalk identity to capture plant-level variation. This high-density phenotypic dataset enabled statistical genomics, predictive modeling, and machine learning to uncover genetic factors underlying lodging resistance, offering insights applicable to other grass species.

The study provides an in‑depth proteomic characterization of brewer's spent grain (BSG) and tracks proteome dynamics during malting and mashing, revealing that 29% of identified proteins change in abundance and that B3‑Hordein dominates the BSG protein pool. BSG contains a high proportion of intracellular proteins and over 45% of its proteins are potential allergens or antinutritional factors, underscoring the need for targeted downstream processing to create safe, functional food ingredients.

The study utilizes explainable artificial intelligence (XAI) combined with machine learning to assess how inter‑annual weather variability influences oilseed sunflower yields across the United States from 1976 to 2022. Key climate predictors, especially summer maximum temperature and total precipitation, were identified, and predictive models were projected under various Shared Socioeconomic Pathways to 2080, revealing region‑specific yield declines.